Masterthesis script, eigen weerparameters
Heleen Van Ransbeeck
2023-01-03
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionEigendata<-read.table(file = "~/GitHub/vespa_analyses/Input/Eigendata.txt", header=TRUE, sep="\t")
Eigendata_shortind<-read.table(file = "~/GitHub/vespa_analyses/Input/Eigendata.txt", header=TRUE, sep="\t")
library(ggplot2)
library(ggpubr)Outlier van nest 29, Bait 1, Individu A weggelaten in deze dataset. (600m op 2min lijkt wel heel snel) Meting in Excel script wel nog terug te vinden.
Outlier van Melle ook weggelaten (meting op 2 km niet betrouwbaar)
Volgende datasets zijn gecreëerd:
- Eigendata: dataset met alle vliegtijden en eigen metingen van de weersparameters
- Eigendata_shortind: dataset met kortste vliegtijden per individu en eigen metingen van de weersparameters
Voor het model: Flight time ~ Distance gebruiken we de dataset kortste vliegtijden per individu Omdat we hiermee de theoretische regel 1min=100m kunnen verifiëren. De imkers nemen hiervoor altijd kortste meting
Voor modellen met weerparameters, gewicht, urbanisatie: Hiervoor nemen we telkens de hele dataset, omdat elke meting van deze factoren afhangt.
1.1) ForagingSpeed vs Temperature
Graphs
Van temperatuur ook categorische variabele maken (aanraden van Prof.Vangestel)
Eigendata$Temperature_cat<-cut(Eigendata$Temperature, c(8,13,17,21,25,29,33))
Eigendata$Temperature_cat## [1] (21,25] (21,25] (21,25] (21,25] (21,25] (21,25] (21,25] (21,25] (21,25]
## [10] (21,25] (17,21] (17,21] (17,21] (17,21] (17,21] (17,21] (17,21] (13,17]
## [19] (29,33] (21,25] (21,25] (21,25] (21,25] (21,25] (29,33] (29,33] (29,33]
## [28] <NA> <NA> <NA> <NA> <NA> (21,25] (25,29] (25,29] (17,21]
## [37] (17,21] (25,29] (25,29] (25,29] (25,29] (25,29] (25,29] (25,29] (29,33]
## [46] (21,25] (21,25] (21,25] (21,25] (21,25] (13,17] (17,21] (13,17] (13,17]
## [55] (13,17] (13,17] (13,17] (17,21] (13,17] (13,17] (13,17] (13,17] (13,17]
## [64] (13,17] (13,17] <NA> (17,21] (13,17] (17,21] (17,21] (17,21] (17,21]
## [73] (17,21] (17,21] (17,21] (17,21] (17,21] (17,21] (17,21] (17,21] (17,21]
## [82] (21,25] (21,25] (21,25] <NA> <NA> (21,25] (21,25] (21,25] (21,25]
## [91] (21,25] (21,25] (21,25] <NA> <NA> (21,25] (21,25] (21,25] <NA>
## [100] <NA> <NA> <NA> <NA> <NA> (17,21] (17,21] (17,21] (13,17]
## [109] (13,17] (13,17] <NA> <NA> (17,21] (17,21] (17,21] (17,21] (17,21]
## [118] (17,21] <NA> <NA> (13,17] (13,17] <NA> (21,25] (21,25] (17,21]
## [127] (17,21] <NA> <NA> (13,17] (17,21] (21,25] (21,25] (21,25] <NA>
## [136] (8,13] <NA> <NA> <NA> (13,17] (13,17] <NA> (13,17] (13,17]
## [145] (13,17] (13,17] <NA> (17,21] <NA> (13,17] (13,17] (13,17] (17,21]
## [154] (17,21] (13,17] (13,17] (13,17] (21,25] (21,25] (21,25] (13,17] (13,17]
## [163] <NA> <NA> <NA> <NA> <NA> (17,21] (17,21] (17,21] (17,21]
## [172] <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## [181] <NA> (17,21] <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## [190] (8,13] (8,13] (8,13] (8,13] (8,13] (8,13] (8,13] (8,13] (8,13]
## [199] (8,13] (8,13] (8,13] (8,13] (8,13] (8,13] (8,13] (8,13] (13,17]
## [208] <NA> <NA> <NA> <NA> <NA> <NA> <NA> (17,21] (17,21]
## [217] <NA> (8,13] (8,13] (8,13] (13,17] (13,17] (13,17] (13,17] (13,17]
## [226] (13,17] (8,13] (8,13] (8,13] (8,13] (8,13] (8,13] (8,13] (8,13]
## [235] (8,13] (8,13] (13,17] (13,17] (13,17] (13,17] (13,17] (8,13] (8,13]
## [244] (8,13] (8,13] (8,13] (8,13] (8,13] (8,13] (8,13] (8,13] (8,13]
## [253] <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## Levels: (8,13] (13,17] (17,21] (21,25] (25,29] (29,33]ggplot(data=subset(Eigendata, !is.na(Temperature_cat)), aes(x=Temperature_cat)) + geom_bar(fill="lightcoral")
Model Output
- Temperature = continuous variable
Niet significant
Normality niet ok!
- Temperature = discrete variable
Een aantal klassen significant, interpretatie?
Normality niet ok!
Poisson verdeling ook geprobeerd met glmer maar foutmelding (zie email)
library(lmerTest)## Loading required package: lme4## Loading required package: Matrix##
## Attaching package: 'lmerTest'## The following object is masked from 'package:lme4':
##
## lmer## The following object is masked from 'package:stats':
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## steplibrary(lme4)
model_temp_cont<-lmer(Flighttime_min ~ Temperature + (1|Individualcode), na.action=na.omit, data=Eigendata, offset= Distance)
summary(model_temp_cont)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Flighttime_min ~ Temperature + (1 | Individualcode)
## Data: Eigendata
## Offset: Distance
##
## REML criterion at convergence: 1366.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.9375 -0.2898 -0.0230 0.1500 3.9428
##
## Random effects:
## Groups Name Variance Std.Dev.
## Individualcode (Intercept) 67952.248 260.677
## Residual 3.702 1.924
## Number of obs: 183, groups: Individualcode, 58
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -332.03927 34.45121 58.47880 -9.638 1.1e-13 ***
## Temperature 0.09884 0.19540 124.22687 0.506 0.614
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## Temperature -0.113anova(model_temp_cont, ddf="Satterthwaite", type=3)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Temperature 0.94747 0.94747 1 124.23 0.2559 0.6138res<-residuals(model_temp_cont)
shapiro.test(res)##
## Shapiro-Wilk normality test
##
## data: res
## W = 0.80863, p-value = 3.188e-14qqnorm(res)
qqline(res)
model_temp_cat<-lmer(Flighttime_min ~ Temperature_cat + (1|Individualcode), offset=Distance, data=Eigendata)
summary(model_temp_cat)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Flighttime_min ~ Temperature_cat + (1 | Individualcode)
## Data: Eigendata
## Offset: Distance
##
## REML criterion at convergence: 1329.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.7833 -0.2906 -0.0097 0.1433 3.7080
##
## Random effects:
## Groups Name Variance Std.Dev.
## Individualcode (Intercept) 68858.22 262.409
## Residual 3.37 1.836
## Number of obs: 183, groups: Individualcode, 58
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -341.5757 37.1284 56.0945 -9.200 8.45e-13 ***
## Temperature_cat(13,17] 3.2036 0.8981 121.0057 3.567 0.000518 ***
## Temperature_cat(17,21] 4.7465 1.4043 121.0385 3.380 0.000976 ***
## Temperature_cat(21,25] 3.3026 2.3108 121.1265 1.429 0.155523
## Temperature_cat(25,29] 63.4493 99.9309 56.0040 0.635 0.528058
## Temperature_cat(29,33] 3.4708 2.7746 121.1038 1.251 0.213371
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) T_(13, T_(17, T_(21, T_(25,
## Tmp_(13,17] -0.021
## Tmp_(17,21] -0.026 0.639
## Tmp_(21,25] -0.028 0.389 0.608
## Tmp_(25,29] -0.372 0.008 0.010 0.010
## Tmp_(29,33] -0.025 0.324 0.506 0.833 0.009anova(model_temp_cat, ddf="Satterthwaite", type=3)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Temperature_cat 53.121 10.624 5 97.929 3.1524 0.01109 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1res<-residuals(model_temp_cat)
shapiro.test(res)##
## Shapiro-Wilk normality test
##
## data: res
## W = 0.84698, p-value = 1.395e-12qqnorm(res)
qqline(res)
1.2) Flight time vs Distance + Temperature
Graph
library(knitr)
library(kableExtra)## Warning in !is.null(rmarkdown::metadata$output) && rmarkdown::metadata$output
## %in% : 'length(x) = 2 > 1' in coercion to 'logical(1)'##
## Attaching package: 'kableExtra'## The following object is masked from 'package:dplyr':
##
## group_rowslibrary("plot3D")
x <- Eigendata$Distance
y <- Eigendata$Temperature
z <- Eigendata$Flighttime_min
fit <- lm(z ~ x + y, na.action=na.exclude)
x.pred <- seq(min(x[!is.na(x)]), max(x[!is.na(x)]), length.out = 20)
y.pred <- seq(min(y[!is.na(y)]), max(y[!is.na(y)]), length.out = 20)
xy <- expand.grid( x = x.pred, y = y.pred)
z.pred <- matrix(predict(fit, newdata = xy),
nrow = 20, ncol = 20)
fitpoints <- predict(fit)
scatter3D(x, y, z, pch = 19, cex = 0.6, colvar=FALSE, col="dodgerblue3", theta = 210, phi = 10, bty="u", col.panel ="grey93", expand =0.4, col.grid = "white", xlab = "Distance", ylab = "Temperature", zlab = "Flight time", surf = list(x = x.pred, y = y.pred, z = z.pred,
facets = TRUE, col=ramp.col(col = c("dodgerblue4", "seagreen2"), n = 100, alpha=0.8), fit = fitpoints, border="black"),main = "Flight time vs Distance + Temperature")
Met plotly
Had met deze link problemen met expand.grid https://stackoverflow.com/questions/38331198/add-regression-plane-to-3d-scatter-plot-in-plotly
Dan maar grid dat van hierboven gebruikt, maar nu klopt er precies iets niet? Alle datapunten liggen boven het oppervlak.
library(plotly)##
## Attaching package: 'plotly'## The following object is masked from 'package:ggplot2':
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## last_plot## The following object is masked from 'package:stats':
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## filter## The following object is masked from 'package:graphics':
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## layoutlibrary(tidyverse)## ── Attaching packages
## ───────────────────────────────────────
## tidyverse 1.3.2 ──## ✔ tibble 3.1.8 ✔ purrr 1.0.1
## ✔ tidyr 1.2.1 ✔ stringr 1.5.0
## ✔ readr 2.1.4 ✔ forcats 1.0.0
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ tidyr::expand() masks Matrix::expand()
## ✖ plotly::filter() masks dplyr::filter(), stats::filter()
## ✖ kableExtra::group_rows() masks dplyr::group_rows()
## ✖ dplyr::lag() masks stats::lag()
## ✖ tidyr::pack() masks Matrix::pack()
## ✖ tidyr::unpack() masks Matrix::unpack()FlightsNoNA<-Eigendata %>% filter(Flighttime_min!="NA")
FlightsNoNA<-FlightsNoNA %>% filter(Temperature!="NA")
fig <- plot_ly(FlightsNoNA, x = ~Distance, y = ~Temperature, z = ~Flighttime_min, size=1)
fig <- fig %>% add_markers()
fig <- fig %>% layout(scene = list(xaxis = list(title = 'Distance'),
yaxis = list(title = 'Temperature'),
zaxis = list(title = 'Flight time (min)')))
p2 <- add_trace(p = fig,
z = z.pred,
x = seq(2100, 0, by = -100),
y = seq(0, 30, by = 10),
type = "surface")
p2Model Ouput
Beide significant
Normality niet ok!
model_tempdist_all<-lmer(Flighttime_min ~ Distance + Temperature + (1|Individualcode), na.action=na.exclude, data=Eigendata)
summary(model_tempdist_all)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Flighttime_min ~ Distance + Temperature + (1 | Individualcode)
## Data: Eigendata
##
## REML criterion at convergence: 803.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.8894 -0.3936 -0.1599 0.1706 4.6284
##
## Random effects:
## Groups Name Variance Std.Dev.
## Individualcode (Intercept) 1.445 1.202
## Residual 3.452 1.858
## Number of obs: 183, groups: Individualcode, 58
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.561776 0.845837 62.971937 5.393 1.11e-06 ***
## Distance 0.006563 0.000976 78.841216 6.725 2.52e-09 ***
## Temperature -0.147567 0.045732 80.427866 -3.227 0.00181 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Distnc
## Distance 0.050
## Temperature -0.909 -0.379res<-residuals(model_tempdist_all)
shapiro.test(res)##
## Shapiro-Wilk normality test
##
## data: res
## W = 0.76816, p-value = 1.014e-15qqnorm(res)
qqline(res)
2) ForagingSpeed vs Cloudcoverage
Graphs
ggplot(Eigendata, aes(x=Cloudcoverage, y=ForagingSpeed)) + geom_point(color="lightblue") + geom_smooth(method="lm", formula =y ~ x, se=TRUE, fullrange=FALSE, level=0.95, color="darkblue") + ggtitle("All data | eigen metingen")+ theme(plot.title = element_text(hjust = 0.5, size=10))
Model Ouput
Niet significant
Normality ok!
model_cloud_all<-lmer(Flighttime_min ~ Cloudcoverage + (1|Individualcode), offset=Distance, na.action=na.omit, data=Eigendata)
summary(model_cloud_all)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Flighttime_min ~ Cloudcoverage + (1 | Individualcode)
## Data: Eigendata
## Offset: Distance
##
## REML criterion at convergence: 1055.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.8981 -0.3081 -0.0411 0.1862 3.6991
##
## Random effects:
## Groups Name Variance Std.Dev.
## Individualcode (Intercept) 81926.936 286.229
## Residual 3.905 1.976
## Number of obs: 152, groups: Individualcode, 39
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -315.994 45.836 38.007 -6.894 3.42e-08 ***
## Cloudcoverage -1.897 1.090 112.017 -1.741 0.0844 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## Cloudcoverg -0.010anova(model_cloud_all, ddf="Satterthwaite", type=3)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Cloudcoverage 11.84 11.84 1 112.02 3.032 0.08438 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1res<-residuals(model_cloud_all)
shapiro.test(res)##
## Shapiro-Wilk normality test
##
## data: res
## W = 0.8356, p-value = 9.028e-12qqnorm(res)
qqline(res)
3.1) ForagingSpeed vs Windspeed
Graphs
Telkens voor de modellen
ForagingSpeed ~ Windspeed
ForagingSpeed ~ Windspeed²
plot1<-ggplot(Eigendata, aes(x=Windspeed, y=ForagingSpeed)) + geom_point(color="khaki3") + geom_smooth(method="lm", formula =y ~ x, se=TRUE, fullrange=FALSE, level=0.95, color="olivedrab") + ggtitle("All data | eigen metingen")+ theme(plot.title = element_text(hjust = 0.5, size=10))
plot2<-ggplot(Eigendata, aes(x=Windspeed, y=ForagingSpeed)) + geom_point(color="khaki3") + geom_smooth(method="lm", formula =y ~ I(x^2), se=TRUE, fullrange=FALSE, level=0.95, color="olivedrab") + ggtitle("All data | eigen metingen")+ theme(plot.title = element_text(hjust = 0.5, size=10))
ggarrange(plot1, plot2 + rremove("x.text"),
labels = c("A", "B"),
ncol = 2, nrow = 1)
Model Output
Beide modellen licht significant
Beide normality niet ok!
model_wind_all<-lmer(Flighttime_min ~ Windspeed + (1|Individualcode), offset=Distance, na.action=na.omit, data=Eigendata)
summary(model_wind_all)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Flighttime_min ~ Windspeed + (1 | Individualcode)
## Data: Eigendata
## Offset: Distance
##
## REML criterion at convergence: 1262.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.0307 -0.3235 -0.0332 0.1735 3.4539
##
## Random effects:
## Groups Name Variance Std.Dev.
## Individualcode (Intercept) 68307.191 261.36
## Residual 3.571 1.89
## Number of obs: 174, groups: Individualcode, 52
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -327.7082 36.2538 51.0530 -9.039 3.56e-12 ***
## Windspeed 0.9518 0.3951 121.0224 2.409 0.0175 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## Windspeed -0.023anova(model_wind_all, ddf="Satterthwaite", type=3)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Windspeed 20.731 20.731 1 121.02 5.805 0.01749 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1model_wind2_all<-lmer(Flighttime_min ~ I(Windspeed^2) + (1|Individualcode), offset=Distance, na.action=na.omit, data=Eigendata)
summary(model_wind2_all)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Flighttime_min ~ I(Windspeed^2) + (1 | Individualcode)
## Data: Eigendata
## Offset: Distance
##
## REML criterion at convergence: 1264.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.0393 -0.3245 -0.0275 0.1740 3.4495
##
## Random effects:
## Groups Name Variance Std.Dev.
## Individualcode (Intercept) 68276.457 261.298
## Residual 3.551 1.884
## Number of obs: 174, groups: Individualcode, 52
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -326.99309 36.23952 51.01860 -9.023 3.79e-12 ***
## I(Windspeed^2) 0.24383 0.09524 121.02600 2.560 0.0117 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## I(Wndspd^2) -0.014anova(model_wind2_all, ddf="Satterthwaite", type=3)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## I(Windspeed^2) 23.272 23.272 1 121.03 6.554 0.0117 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1res<-residuals(model_wind_all)
shapiro.test(res)##
## Shapiro-Wilk normality test
##
## data: res
## W = 0.83203, p-value = 7.048e-13qqnorm(res)
qqline(res)
res<-residuals(model_wind2_all)
shapiro.test(res)##
## Shapiro-Wilk normality test
##
## data: res
## W = 0.83542, p-value = 9.856e-13qqnorm(res)
qqline(res)
3.2) ForagingSpeed vs Windspeed | afhankelijk van de windrichting
Hiervoor werd telkens voor elke meting nagegaan of de hoornaar met meewind (tailwind), tegenwind (upwind) of loodrechte wind (perpendicular) te maken had. Dit volgens de formules:
|𝜃flight −𝜃wind| ≤ 45 is tailwind
45 < |𝜃flight −𝜃wind|<135 is (quasi) perpendicular
|𝜃 flight −𝜃wind| ≥135 upwind
Graphs
ggplot(data=subset(Eigendata, !is.na(Wind_flight)), aes(x= Wind_flight, col=Wind_flight, y=ForagingSpeed)) + geom_boxplot()## Warning: Removed 6 rows containing non-finite values (`stat_boxplot()`).
ggplot(data=subset(Eigendata, !is.na(Wind_nest)), aes(x= Wind_nest, col=Wind_nest, y=ForagingSpeed)) + geom_boxplot()## Warning: Removed 6 rows containing non-finite values (`stat_boxplot()`).
ggplot(data=subset(Eigendata, !is.na(Wind_flight)), aes(x= Windspeed, col=Wind_flight, y=ForagingSpeed)) + geom_point() + facet_grid(~Wind_flight) + geom_smooth(method="lm", formula = y ~ I(x^2), se=TRUE, fullrange=FALSE, level=0.95)## Warning: Removed 6 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 6 rows containing missing values (`geom_point()`).
ggplot(data=subset(Eigendata, !is.na(Wind_nest)), aes(x= Windspeed, col=Wind_nest, y=ForagingSpeed)) + geom_point() + facet_grid(~Wind_nest) + geom_smooth(method="lm", formula = y ~ I(x^2), se=TRUE, fullrange=FALSE, level=0.95)## Warning: Removed 6 rows containing non-finite values (`stat_smooth()`).
## Removed 6 rows containing missing values (`geom_point()`).
Model Output
Anova van Foragingspeed en de 3 windinvloeden
Dit voor windrichting t.o.v. richting tot het nest en t.o.v. vliegrichting
Niet significant
Normality niet oke
interpretatie?
windflightanova<-lmer(Flighttime_min ~ Wind_flight + (1|Individualcode), na.action=na.exclude, offset=Distance, data=Eigendata)
summary(windflightanova)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Flighttime_min ~ Wind_flight + (1 | Individualcode)
## Data: Eigendata
## Offset: Distance
##
## REML criterion at convergence: 1151.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.8767 -0.3156 -0.0390 0.1724 3.8245
##
## Random effects:
## Groups Name Variance Std.Dev.
## Individualcode (Intercept) 65409.102 255.752
## Residual 3.965 1.991
## Number of obs: 161, groups: Individualcode, 46
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -319.3628 37.7103 45.0036 -8.469 7.23e-11 ***
## Wind_flighttailwind 0.1535 1.3936 113.0331 0.110 0.913
## Wind_flightupwind -0.8702 0.8884 113.0167 -0.980 0.329
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Wnd_flghtt
## Wnd_flghttl -0.006
## Wnd_flghtpw -0.006 0.089res<-residuals(windflightanova)
shapiro.test(res)##
## Shapiro-Wilk normality test
##
## data: res
## W = 0.82875, p-value = 1.873e-12qqnorm(res)
qqline(res)
windnestanova<-lmer(Flighttime_min ~ Wind_nest + (1|Individualcode), na.action=na.exclude, offset=Distance, data=Eigendata)
summary(windnestanova)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Flighttime_min ~ Wind_nest + (1 | Individualcode)
## Data: Eigendata
## Offset: Distance
##
## REML criterion at convergence: 1149.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.8920 -0.3231 -0.0462 0.1811 3.8447
##
## Random effects:
## Groups Name Variance Std.Dev.
## Individualcode (Intercept) 65436.126 255.805
## Residual 3.923 1.981
## Number of obs: 161, groups: Individualcode, 46
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -319.1543 37.7196 45.0110 -8.461 7.41e-11 ***
## Wind_nesttailwind 0.4132 1.4495 113.0518 0.285 0.776
## Wind_nestupwind -1.6444 1.1318 113.0384 -1.453 0.149
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Wnd_nstt
## Wnd_nsttlwn -0.008
## Wnd_nstpwnd -0.008 0.000res<-residuals(windnestanova)
shapiro.test(res)##
## Shapiro-Wilk normality test
##
## data: res
## W = 0.83112, p-value = 2.345e-12qqnorm(res)
qqline(res)
ForagingSpeed vs WindSpeed voor Tailwind dataset
Significant
Normality niet ok!
data_tailwind<-subset(Eigendata, Wind_flight == "tailwind")
model_tailwind<-lmer(Flighttime_min ~ Windspeed + (1|Individualcode), offset= Distance, na.action=na.omit, data=data_tailwind)
summary(model_tailwind)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Flighttime_min ~ Windspeed + (1 | Individualcode)
## Data: data_tailwind
## Offset: Distance
##
## REML criterion at convergence: 158.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.74181 -0.18655 -0.03152 0.01763 1.88834
##
## Random effects:
## Groups Name Variance Std.Dev.
## Individualcode (Intercept) 46780.21 216.287
## Residual 10.78 3.283
## Number of obs: 21, groups: Individualcode, 8
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -1109.162 240.549 5.999 -4.611 0.00365 **
## Windspeed 314.022 104.860 5.999 2.995 0.02418 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## Windspeed -0.948anova(model_tailwind, ddf="Satterthwaite", type=3)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Windspeed 96.645 96.645 1 5.9991 8.968 0.02418 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1res<-residuals(model_tailwind)
shapiro.test(res)##
## Shapiro-Wilk normality test
##
## data: res
## W = 0.82908, p-value = 0.001892qqnorm(res)
qqline(res)
ForagingSpeed vs WindSpeed voor Perpendicular dataset
Significant
Normality niet ok!
data_perpendicular<-subset(Eigendata, Wind_flight == "perpendicular")
model_perpendicular<-lmer(Flighttime_min ~ Windspeed + (1|Individualcode), offset= Distance, na.action=na.omit, data=data_perpendicular)## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## unable to evaluate scaled gradient## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge: degenerate Hessian with 1 negative eigenvaluessummary(model_perpendicular)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Flighttime_min ~ Windspeed + (1 | Individualcode)
## Data: data_perpendicular
## Offset: Distance
##
## REML criterion at convergence: 730.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.0291 -0.3957 -0.0109 0.2275 3.5200
##
## Random effects:
## Groups Name Variance Std.Dev.
## Individualcode (Intercept) 55413.701 235.401
## Residual 2.319 1.523
## Number of obs: 104, groups: Individualcode, 33
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -307.2799 40.9876 32.0278 -7.497 1.55e-08 ***
## Windspeed 1.9726 0.4035 70.0147 4.888 6.23e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## Windspeed -0.021
## optimizer (nloptwrap) convergence code: 0 (OK)
## unable to evaluate scaled gradient
## Model failed to converge: degenerate Hessian with 1 negative eigenvaluesres<-residuals(model_perpendicular)
shapiro.test(res)##
## Shapiro-Wilk normality test
##
## data: res
## W = 0.88169, p-value = 1.377e-07qqnorm(res)
qqline(res)
ForagingSpeed vs WindSpeed voor Upwind dataset
Niet significant
Normality niet ok!
data_upwind<-subset(Eigendata, Wind_flight == "upwind")
model_upwind<-lmer(Flighttime_min ~ Windspeed + (1|Individualcode), offset=Distance, na.action=na.omit, data=data_upwind)
summary(model_upwind)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Flighttime_min ~ Windspeed + (1 | Individualcode)
## Data: data_upwind
## Offset: Distance
##
## REML criterion at convergence: 248.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.65651 -0.32560 -0.01373 0.16570 2.43454
##
## Random effects:
## Groups Name Variance Std.Dev.
## Individualcode (Intercept) 38919.718 197.281
## Residual 2.771 1.665
## Number of obs: 36, groups: Individualcode, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -201.008 57.112 11.125 -3.520 0.00472 **
## Windspeed -1.029 2.015 23.052 -0.511 0.61442
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## Windspeed -0.075res<-residuals(model_upwind)
shapiro.test(res)##
## Shapiro-Wilk normality test
##
## data: res
## W = 0.8614, p-value = 0.0003521qqnorm(res)
qqline(res)
4) ForagingSpeed vs Weight individual
Graphs
ggplot(Eigendata, aes(x=Weight_ind, y=ForagingSpeed)) + geom_point(color="plum") + geom_smooth(method="lm", formula =y ~ x, se=TRUE, fullrange=FALSE, level=0.95, color="darkorchid4") + ggtitle("All data")+ theme(plot.title = element_text(hjust = 0.5, size=10))
Model Output
Niet significant
Normality ok!
model_weight_all<-lmer(Flighttime_min ~ Weight_ind + (1|Individualcode), offset=Distance, na.action=na.omit, data=Eigendata)
summary(model_weight_all)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Flighttime_min ~ Weight_ind + (1 | Individualcode)
## Data: Eigendata
## Offset: Distance
##
## REML criterion at convergence: 557.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2045 -0.3196 -0.0862 0.1576 3.4639
##
## Random effects:
## Groups Name Variance Std.Dev.
## Individualcode (Intercept) 79697.832 282.308
## Residual 3.194 1.787
## Number of obs: 89, groups: Individualcode, 20
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -301.93 341.39 18.00 -0.884 0.388
## Weight_ind 39.82 1000.02 18.00 0.040 0.969
##
## Correlation of Fixed Effects:
## (Intr)
## Weight_ind -0.983anova(model_weight_all, ddf="Satterthwaite", type=3)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Weight_ind 0.0050637 0.0050637 1 17.999 0.0016 0.9687res<-residuals(model_weight_all)
shapiro.test(res)##
## Shapiro-Wilk normality test
##
## data: res
## W = 0.77318, p-value = 2.123e-10qqnorm(res)
qqline(res)
5.1) ForagingSpeed vs Urbanisation (Traject 100m)
Graphs
Urbanisatiecirkels niet berekend, meteen trajecten genomen want die zijn betere maatstaf
Verder werken met 100m buffer!
# Meting in urbaan gebied met koude temperatuur weglaten (Nest 38 en 39, 3 wiekpotten)
Eigendata2<-Eigendata %>% filter(Traject100m!=0.504960)
Eigendata2<-Eigendata2 %>% filter(Traject100m!=0.496950)
Eigendata2<-Eigendata2 %>% filter(Traject100m!=0.411898)
Eigendata_shortind2<-Eigendata_shortind %>% filter(Traject100m!=0.504960)
Eigendata_shortind2<-Eigendata_shortind2 %>% filter(Traject100m!=0.496950)
Eigendata_shortind2<-Eigendata_shortind2 %>% filter(Traject100m!=0.411898)
plot1<-ggplot(data=subset(Eigendata, !is.na(Observer)), aes(x=Traject100m, y=ForagingSpeed)) + geom_point(col="slateblue1") + geom_abline(intercept=3.8984, slope= -5.3143, color="slateblue4", linewidth=1) + ggtitle("Met outlier | random effects") + xlim(0, 0.6)
plot2<-ggplot(data=subset(Eigendata, !is.na(Observer)), aes(x=Traject100m, y=ForagingSpeed)) + geom_point(col="slateblue1") + geom_smooth(method="lm", formula =y ~ x, se=TRUE, fullrange=FALSE, level=0.95, color="slateblue4") + ggtitle("Met outlier | zonder random effects") + xlim(0, 0.6)
plot3<-ggplot(data=subset(Eigendata2, !is.na(Observer)), aes(x=Traject100m, y=ForagingSpeed)) + geom_point(col="slateblue1") + geom_abline(intercept=3.7838, slope= -4.0061, color="slateblue4", linewidth=1) + ggtitle("Zonder outlier | random effects") + xlim(0, 0.6)
plot4<-ggplot(data=subset(Eigendata2, !is.na(Observer)), aes(x=Traject100m, y=ForagingSpeed)) + geom_point(col="slateblue1") + geom_smooth(method="lm", formula =y ~ x, se=TRUE, fullrange=FALSE, level=0.95, color="slateblue4") + ggtitle("Zonder outlier | zonder random effects") + xlim(0, 0.6)
ggarrange(plot1, plot2, plot3, plot4 + rremove("x.text"),
labels = c("A", "B", "C", "D"),
ncol = 2, nrow = 2)## Warning: Removed 26 rows containing missing values (`geom_point()`).## Warning: Removed 26 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 26 rows containing missing values (`geom_point()`).
## Removed 26 rows containing missing values (`geom_point()`).## Warning: Removed 26 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 26 rows containing missing values (`geom_point()`).
Verder werken met 100m buffer!
Graph Temperature vs Urbanisation
ggplot(data=subset(Eigendata, !is.na(Temperature_cat)), aes(x=Temperature_cat, col=Temperature_cat, y=Traject100m)) + geom_boxplot()
Model Output
Met outlier
Hoogsignificant
Normality niet ok!
model_urb1<-lmer(Flighttime_min ~ Traject100m + (1|Individualcode), offset=Distance, na.action=na.omit, data=Eigendata)
summary(model_urb1)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Flighttime_min ~ Traject100m + (1 | Individualcode)
## Data: Eigendata
## Offset: Distance
##
## REML criterion at convergence: 1918
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.0862 -0.2381 -0.0067 0.1117 4.1024
##
## Random effects:
## Groups Name Variance Std.Dev.
## Individualcode (Intercept) 69478.334 263.587
## Residual 3.445 1.856
## Number of obs: 234, groups: Individualcode, 94
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -551.80 42.05 92.00 -13.124 < 2e-16 ***
## Traject100m 1018.13 225.44 92.00 4.516 1.86e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## Traject100m -0.763anova(model_urb1, ddf="Satterthwaite", type=3)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Traject100m 70.262 70.262 1 91.998 20.397 1.863e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1res<-residuals(model_urb1)
shapiro.test(res)##
## Shapiro-Wilk normality test
##
## data: res
## W = 0.77701, p-value < 2.2e-16qqnorm(res)
qqline(res)
Zonder outlier
Significant
Normality niet ok!
model_urb2<-lmer(Flighttime_min ~ Traject100m + (1|Individualcode), offset=Distance, na.action=na.omit, data=Eigendata2)
summary(model_urb2)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Flighttime_min ~ Traject100m + (1 | Individualcode)
## Data: Eigendata2
## Offset: Distance
##
## REML criterion at convergence: 1679.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.7342 -0.2214 -0.0047 0.0920 4.0368
##
## Random effects:
## Groups Name Variance Std.Dev.
## Individualcode (Intercept) 73469.487 271.053
## Residual 2.352 1.534
## Number of obs: 199, groups: Individualcode, 89
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -550.98 46.42 87.00 -11.870 < 2e-16 ***
## Traject100m 1009.41 291.35 87.00 3.465 0.000827 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## Traject100m -0.785anova(model_urb2, ddf="Satterthwaite", type=3)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Traject100m 28.229 28.229 1 86.999 12.004 0.0008265 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1res<-residuals(model_urb2)
shapiro.test(res)##
## Shapiro-Wilk normality test
##
## data: res
## W = 0.71442, p-value < 2.2e-16qqnorm(res)
qqline(res)
5.2) ForagingSpeed vs Urbanisation (Categorisch)
ggplot(data=Eigendata, aes(x=Urbanisation, y=ForagingSpeed, col=Urbanisation)) + geom_boxplot() ## Warning: Removed 26 rows containing non-finite values (`stat_boxplot()`).
model_urb_cat<-lmer(Flighttime_min ~ Urbanisation + (1|Individualcode), offset=Distance, na.action=na.omit, data=Eigendata )
summary(model_urb_cat)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Flighttime_min ~ Urbanisation + (1 | Individualcode)
## Data: Eigendata
## Offset: Distance
##
## REML criterion at convergence: 1906.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.0847 -0.2381 -0.0062 0.1113 4.1025
##
## Random effects:
## Groups Name Variance Std.Dev.
## Individualcode (Intercept) 67030.070 258.902
## Residual 3.445 1.856
## Number of obs: 234, groups: Individualcode, 94
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -524.37 40.94 91.00 -12.809 < 2e-16 ***
## UrbanisationRandstedelijk 106.72 61.41 91.00 1.738 0.0856 .
## UrbanisationVerstedelijkt 346.48 68.72 91.00 5.042 2.34e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) UrbnsR
## UrbnstnRnds -0.667
## UrbnstnVrst -0.596 0.3976) Flight error vs urbanisation (25m, 50m, 100m)
Flight error is the difference between the actual angle from the bait to the nest and the flight direction I measured.
Graphs
plot28<-ggplot(data=subset(Eigendata, !is.na(Observer)), aes(x=Traject25m, y=FlightError)) + geom_point(col="hotpink1") + geom_smooth(method="lm", formula =y ~ x, se=TRUE, fullrange=FALSE, level=0.95, color="violetred4")
plot29<-ggplot(data=subset(Eigendata, !is.na(Observer)), aes(x=Traject50m, y=FlightError)) + geom_point(col="hotpink1") + geom_smooth(method="lm", formula =y ~ x , se=TRUE, fullrange=FALSE, level=0.95, color="violetred4")
plot30<-ggplot(data=subset(Eigendata, !is.na(Observer)), aes(x=Traject100m, y=FlightError)) + geom_point(col="hotpink1")+ geom_smooth(method="lm", formula =y ~ x, se=TRUE, fullrange=FALSE, level=0.95, color="violetred4")
ggarrange(plot28, plot29, plot30 + rremove("x.text"),
labels = c("A", "B", "C"),
ncol = 2, nrow = 2)## Warning: Removed 9 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 9 rows containing missing values (`geom_point()`).## Warning: Removed 9 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 9 rows containing missing values (`geom_point()`).## Warning: Removed 9 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 9 rows containing missing values (`geom_point()`).
Model Output
Allemaal niet significant
Allemaal normality niet ok!
model_errurb25<-lmer(FlightError ~ Traject25m + (1|NestID) + (1|NestID:BaitID) + (1|Individualcode), na.action=na.omit, data=Eigendata)
summary(model_errurb25)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: FlightError ~ Traject25m + (1 | NestID) + (1 | NestID:BaitID) +
## (1 | Individualcode)
## Data: Eigendata
##
## REML criterion at convergence: 1947.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.7734 -0.1939 -0.0500 0.0181 6.9743
##
## Random effects:
## Groups Name Variance Std.Dev.
## Individualcode (Intercept) 133.89 11.571
## NestID:BaitID (Intercept) 119.73 10.942
## NestID (Intercept) 14.35 3.788
## Residual 55.40 7.443
## Number of obs: 251, groups: Individualcode, 111; NestID:BaitID, 85; NestID, 34
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 17.796 2.744 39.401 6.484 1.05e-07 ***
## Traject25m -3.487 14.699 48.243 -0.237 0.813
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## Traject25m -0.704anova(model_errurb25, ddf="Satterthwaite", type=3)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Traject25m 3.119 3.119 1 48.243 0.0563 0.8135model_errurb50<-lmer(FlightError ~ Traject50m + (1|NestID) + (1|NestID:BaitID) + (1|Individualcode), na.action=na.omit, data=Eigendata)
summary(model_errurb50)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: FlightError ~ Traject50m + (1 | NestID) + (1 | NestID:BaitID) +
## (1 | Individualcode)
## Data: Eigendata
##
## REML criterion at convergence: 1946.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.7832 -0.1962 -0.0514 0.0206 6.9719
##
## Random effects:
## Groups Name Variance Std.Dev.
## Individualcode (Intercept) 134.66 11.604
## NestID:BaitID (Intercept) 120.80 10.991
## NestID (Intercept) 12.35 3.514
## Residual 55.33 7.438
## Number of obs: 251, groups: Individualcode, 111; NestID:BaitID, 85; NestID, 34
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 16.620 2.846 40.187 5.841 7.8e-07 ***
## Traject50m 5.634 15.381 43.317 0.366 0.716
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## Traject50m -0.734anova(model_errurb50, ddf="Satterthwaite", type=3)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Traject50m 7.4251 7.4251 1 43.317 0.1342 0.7159model_errurb100<-lmer(FlightError ~ Traject100m + (1|NestID) + (1|NestID:BaitID) + (1|Individualcode), na.action=na.omit, data=Eigendata)
summary(model_errurb100)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: FlightError ~ Traject100m + (1 | NestID) + (1 | NestID:BaitID) +
## (1 | Individualcode)
## Data: Eigendata
##
## REML criterion at convergence: 1946.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.7833 -0.1966 -0.0513 0.0225 6.9715
##
## Random effects:
## Groups Name Variance Std.Dev.
## Individualcode (Intercept) 134.40 11.593
## NestID:BaitID (Intercept) 121.48 11.022
## NestID (Intercept) 11.92 3.452
## Residual 55.33 7.439
## Number of obs: 251, groups: Individualcode, 111; NestID:BaitID, 85; NestID, 34
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 16.575 2.962 40.888 5.597 1.63e-06 ***
## Traject100m 5.851 16.003 41.504 0.366 0.717
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## Traject100m -0.759anova(model_errurb100, ddf="Satterthwaite", type=3)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Traject100m 7.3957 7.3957 1 41.504 0.1337 0.7165res<-residuals(model_errurb25)
shapiro.test(res)##
## Shapiro-Wilk normality test
##
## data: res
## W = 0.58149, p-value < 2.2e-16qqnorm(res)
qqline(res)
res<-residuals(model_errurb50)
shapiro.test(res)##
## Shapiro-Wilk normality test
##
## data: res
## W = 0.58086, p-value < 2.2e-16qqnorm(res)
qqline(res)
res<-residuals(model_errurb100)
shapiro.test(res)##
## Shapiro-Wilk normality test
##
## data: res
## W = 0.581, p-value < 2.2e-16qqnorm(res)
qqline(res)
Model Selection
fullmodel<-lmer(ForagingSpeed ~ Traject100m + Windspeed + Temperature + Cloudcoverage + Weight_ind + (1|NestID) + (1|NestID:BaitID) + (1|Individualcode), na.action=na.omit, data=Eigendata)## boundary (singular) fit: see help('isSingular')summary(fullmodel)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## ForagingSpeed ~ Traject100m + Windspeed + Temperature + Cloudcoverage +
## Weight_ind + (1 | NestID) + (1 | NestID:BaitID) + (1 | Individualcode)
## Data: Eigendata
##
## REML criterion at convergence: 170
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.7356 -0.5550 0.1066 0.5685 1.8733
##
## Random effects:
## Groups Name Variance Std.Dev.
## Individualcode (Intercept) 0.03398 0.1843
## NestID:BaitID (Intercept) 0.31924 0.5650
## NestID (Intercept) 0.00000 0.0000
## Residual 0.30817 0.5551
## Number of obs: 88, groups: Individualcode, 20; NestID:BaitID, 15; NestID, 10
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.99457 2.20523 29.00627 2.265 0.03117 *
## Traject100m -7.86760 2.58297 18.69255 -3.046 0.00674 **
## Windspeed -0.17727 0.16151 9.98177 -1.098 0.29815
## Temperature -0.03233 0.08430 27.81931 -0.384 0.70425
## Cloudcoverage 1.31882 0.39406 24.27275 3.347 0.00266 **
## Weight_ind -1.44193 2.55293 10.61380 -0.565 0.58394
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Trj100 Wndspd Tmprtr Cldcvr
## Traject100m -0.789
## Windspeed 0.204 -0.409
## Temperature -0.888 0.775 -0.384
## Cloudcoverg 0.241 -0.388 0.127 -0.327
## Weight_ind -0.545 0.188 0.084 0.150 -0.048
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')model1<-lmer(ForagingSpeed ~ Traject100m + Windspeed + Cloudcoverage + Weight_ind + (1|NestID) + (1|NestID:BaitID) + (1|Individualcode), na.action=na.omit, data=Eigendata)## boundary (singular) fit: see help('isSingular')summary(model1)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## ForagingSpeed ~ Traject100m + Windspeed + Cloudcoverage + Weight_ind +
## (1 | NestID) + (1 | NestID:BaitID) + (1 | Individualcode)
## Data: Eigendata
##
## REML criterion at convergence: 167
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.6747 -0.5380 0.1197 0.5758 1.9362
##
## Random effects:
## Groups Name Variance Std.Dev.
## Individualcode (Intercept) 0.03317 0.1821
## NestID:BaitID (Intercept) 0.28865 0.5373
## NestID (Intercept) 0.00000 0.0000
## Residual 0.30791 0.5549
## Number of obs: 88, groups: Individualcode, 20; NestID:BaitID, 15; NestID, 10
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.2462 0.9816 10.7873 4.326 0.00126 **
## Traject100m -7.0951 1.5703 8.0472 -4.518 0.00193 **
## Windspeed -0.1992 0.1439 9.4887 -1.384 0.19790
## Cloudcoverage 1.2675 0.3639 22.4685 3.483 0.00206 **
## Weight_ind -1.3126 2.4408 10.0199 -0.538 0.60249
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Trj100 Wndspd Cldcvr
## Traject100m -0.343
## Windspeed -0.325 -0.195
## Cloudcoverg -0.121 -0.231 0.017
## Weight_ind -0.906 0.115 0.155 0.007
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')model2<-lmer(ForagingSpeed ~ Traject100m + Windspeed + Cloudcoverage + (1|NestID) + (1|NestID:BaitID) + (1|Individualcode), na.action=na.omit, data=Eigendata)
summary(model2)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: ForagingSpeed ~ Traject100m + Windspeed + Cloudcoverage + (1 |
## NestID) + (1 | NestID:BaitID) + (1 | Individualcode)
## Data: Eigendata
##
## REML criterion at convergence: 343.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.46705 -0.53111 0.00685 0.48244 2.85559
##
## Random effects:
## Groups Name Variance Std.Dev.
## Individualcode (Intercept) 0.09978 0.3159
## NestID:BaitID (Intercept) 0.39334 0.6272
## NestID (Intercept) 0.25303 0.5030
## Residual 0.37363 0.6113
## Number of obs: 152, groups: Individualcode, 39; NestID:BaitID, 26; NestID, 15
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.99137 0.43247 22.10904 11.542 7.87e-11 ***
## Traject100m -8.09188 1.46500 13.92745 -5.523 7.64e-05 ***
## Windspeed -0.29289 0.09917 81.93301 -2.953 0.0041 **
## Cloudcoverage 0.32661 0.27303 124.69507 1.196 0.2339
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Trj100 Wndspd
## Traject100m -0.661
## Windspeed -0.454 -0.049
## Cloudcoverg -0.180 -0.230 0.106model3<-lmer(ForagingSpeed ~ Traject100m + Windspeed + (1|NestID) + (1|NestID:BaitID) + (1|Individualcode), na.action=na.omit, data=Eigendata)
summary(model3)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: ForagingSpeed ~ Traject100m + Windspeed + (1 | NestID) + (1 |
## NestID:BaitID) + (1 | Individualcode)
## Data: Eigendata
##
## REML criterion at convergence: 412.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.53652 -0.49591 0.01821 0.49551 2.83727
##
## Random effects:
## Groups Name Variance Std.Dev.
## Individualcode (Intercept) 0.08236 0.2870
## NestID:BaitID (Intercept) 0.93614 0.9675
## NestID (Intercept) 0.02917 0.1708
## Residual 0.37294 0.6107
## Number of obs: 174, groups: Individualcode, 52; NestID:BaitID, 37; NestID, 20
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.87080 0.36651 30.78189 13.290 2.72e-14 ***
## Traject100m -7.39979 1.32907 19.45673 -5.568 2.09e-05 ***
## Windspeed -0.20583 0.09498 138.14425 -2.167 0.032 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Trj100
## Traject100m -0.655
## Windspeed -0.551 -0.018res<-residuals(model3)
shapiro.test(res)##
## Shapiro-Wilk normality test
##
## data: res
## W = 0.98787, p-value = 0.1401qqnorm(res)
qqline(res)
library(cAIC4)## Loading required package: stats4## Loading required package: nlme##
## Attaching package: 'nlme'## The following object is masked from 'package:lme4':
##
## lmList## The following object is masked from 'package:dplyr':
##
## collapselibrary(MASS)##
## Attaching package: 'MASS'## The following object is masked from 'package:plotly':
##
## select## The following object is masked from 'package:dplyr':
##
## selectlibrary(AICcmodavg)##
## Attaching package: 'AICcmodavg'## The following object is masked from 'package:lme4':
##
## checkConvAIC(fullmodel)## [1] 190.0063AIC(model1)## [1] 185.0204AIC(model2)## [1] 359.7193AIC(model3)## [1] 426.0821Multicollinearity check
library(ggcorrplot)
library(car)## Loading required package: carData##
## Attaching package: 'car'## The following object is masked from 'package:purrr':
##
## some## The following object is masked from 'package:dplyr':
##
## recodedatacor<-Eigendata[, c("Temperature", "Cloudcoverage", "Windspeed", "Weight_ind", "Traject100m", "NestID")]
source("~/GitHub/vespa_analyses/Input/HighstatLibV10.R")
corvif(datacor)##
##
## Variance inflation factors
##
## GVIF
## Temperature 6.692872
## Cloudcoverage 1.356257
## Windspeed 1.332447
## Weight_ind 1.530327
## Traject100m 6.778816
## NestID 2.226214cormat <- round(cor(datacor, use = "pairwise.complete.obs"), 2)
ggcorrplot(cormat, lab= TRUE, type = "lower", ggtheme = ggplot2::theme_gray,
colors = c("#6D9EC1", "white", "#E46726"))
plot(Temperature~Traject100m, xlab="Urbanisation", data=Eigendata)
Eerder toeval dat Temperature en Traject100m gecorreleeerd zijn?
Eigendata$NestID<-as.factor(Eigendata$NestID)
ggplot(Eigendata, aes(x=NestID, col=NestID, y=Temperature)) + geom_boxplot()## Warning: Removed 68 rows containing non-finite values (`stat_boxplot()`).